“…Far more than having big data or a crack team of unicorn data scientists, it requires establishing an effective, deeply-ingrained data culture. …”

“…Build interactive dashboards and storytelling reports at scale with the cloud-native BI tool that integrates embedded analytics and ML-powered insights effortlessly Key Features Explore Amazon QuickSight, manage data sources, and build and share dashboards Learn best practices from an AWS certified big data solutions architect Manage and monitor dashboards using the QuickSight API and other AWS services such as Amazon CloudTrail Book DescriptionAmazon Quicksight is an exciting new visualization that rivals PowerBI and Tableau, bringing several exciting features to the table – but sadly, there aren’t many resources out there that can help you learn the ropes. …”

“…If you are an administrator or developer who wants to enter the world of Big Data and BigTables and would like to learn about HBase, this is the book for you…”

“…With developments like smart phones, social media, mobile connectivity, big data, and speech analytics, businesses have more opportunities to enhance the customer experience than ever before. …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: The Modern Data Warehouse -- The need for a data warehouse -- Driven by IT -- Self-service BI -- Cloud-based BI - big data and artificial intelligence -- The modern data warehouse -- Main components of a data warehouse -- Staging area -- Data warehouse -- Cubes -- Consumption layer - BI and analytics -- What is Azure Data Factory -- Limitations of ADF V1.0 -- What's new in V2.0? …”

“…"Two disruptions of absolutely unimaginable scale are radically and totally transforming retail banking: (1) a worldwide convergence of financial services, telecom, retail, healthcare and media and (2) the chaotic mash-up of multiple emerging phenomena including nanotechnology, big data analytics, telemedicine, smart cities, machine-to-machine interaction (the Internet of Things) and the incredibly rapid growth of the middleclass in Asia, South Asia and parts of Africa.The new middleclass represents hundreds of billions of dollars in fresh revenue for retail banks. …”

Tabla de Contenidos: “…Engel - The Management and Use of Data in Education and Education Policy: Introductory Remarks Sigrid Hartong - Digital Education Governance and the Productive Relationalities of School Monitoring Infrastructures Steven Lewis - Data, Diagnosis and Prescription: Governing Schooling through the OECD's PISA for Schools Bernard Veldkamp, Kim Schildkamp, Merel Keijsers, Adrie Visscher and Ton de Jong - Big Data Analytics in Education: Big Challenges and Big Opportunities Elmar Souvignier, Natalie Förster, Karin Hebbecker and Birgit Schütze - Using Digital Data to Support Teaching Practice - quop: An Effective Web-Based Approach to Monitor Student Learning Progress in Reading and Mathematics in Entire Classrooms V. …”

Tabla de Contenidos: “…IoT and AI Solutions for E-health -- Alzheimer's Disease Early Detection Using a Low Cost Three-dimensional Densenet-121 Architecture -- Self-adaptative Early Warning Scoring System for Smart Hospital -- Machine Learning Based Rank Attack Detection for Smart Hospital Infrastructure -- Remote Health Monitoring Systems Based on Bluetooth Low Energy (BLE) Communication Systems -- Modeling and Specification of Bootstrapping and Registration Design Patterns for IoT Applications -- Biomedical and Health Informatics -- EEG-based Hypo-vigilance Detection Using Convolutional Neural Network -- Respiratory Activity Classification Based on Ballistocardiogram Analysis -- A Convolutional Neural Network for Lentigo Diagnosis -- Deep Learning-based Approach for Atrial Fibrillation Detection -- Unsupervised Method Based on Superpixel Segmentation for Corpus Callosum Parcellation in MRI Scans -- Behavior and Activity Monitoring -- Using Learning Techniques to Observe Elderly's Behavior Changes Over Time -- Personalized and Contextualized Persuasion System for Older Adults’ Physical Activity Promoting -- Baseline Modelling and Composite Representation of Unobtrusively (IoT) Sensed Behaviour Changes Related to Urban Physical Well-being -- Wellbeing Technology -- Automatic Daily Activity Schedule Planning for Simulating Smart House with Elderly People Living Alone -- A Novel On-wrist Fall Detection System Using Supervised Dictionary Learning Technique -- Combined Machine Learning and Semantic Modelling for Situation Awareness and Healthcare Decision Support -- Improving Access and Mental Health for Youth Through Virtual Models of Care -- Short Contributions: IoT and AI Solutions for E-health -- Study of Middleware for Internet of Healthcare Things and Their Applications -- Uncertainty in IoT for Smart Healthcare: Challenges, and Opportunities -- Secure E-health Platform -- Hybrid and Secure E-health Data Sharing Architecture in Multi-Clouds Environment -- Blockchain for Internet of Medical Things: A technical Review -- Application of Blockchain Technology in Healthcare: A Comprehensive Study -- Trust Execution Environment and Multi-party Computation for Blockchain E-health Systems -- A Fuzzy-ontology Based Diabetes Monitoring System Using Internet of Things -- Short Contributions: Biomedical and Health Informatics -- A Hybrid Approach for Heart Disease Diagnosis and Prediction Using Machine Learning Techniques -- Context-aware Healthcare Adaptation Model for COPD Diseases -- Study of Healthcare Professionals’ Interaction in the Patient Records with Annotations -- Multirate ECG Processing and Machine Learning Based Efficient Arrhythmia Diagnosis -- Comparative Study of Relevant Methods for MRI/X Brain Image Registration -- Machine Learning Classification Models with SPD/ED Dataset: Comparative Study of Abstract Versus Full Article Approach -- Evaluation of Stationary Wavelet Transforms in Reconstruction of Pure High Frequency Oscillations (HFOs) -- Ensuring the Correctness and Well Modeling of Intelligent Healthcare Management Systems -- Short Contributions: Wellbeing Technology -- An Embedded ANN Raspberry PI for Inertial Sensor Based Human Activity Recognition -- Human Activities Recognition in Android Smartphone using WSVM-HMM Classifier -- Mobile Assistive Application for Blind People in Indoor Navigation -- Older People’s Needs and Opportunities for Assistive Technologies -- Towards a Formal Context-Aware Workflow Model for Ambient Environment -- The PULSE Project: A Case of Use of Big Data Uses Toward a Cohomprensive Health Vision of City Well Being -- ForeSight - An AI-driven Smart Living Platform, Approach to Add Access Control to openHAB…”

Tabla de Contenidos: “…/ Mark Taylor and Tess Whitton -- Mobilising public expertise in health research regulation / Michael Burges -- Towards adaptive governance in big data health research : implementing regulatory principles / Effy Vayena and Alessandro Blasimme -- Regulating automated healthcare and research technologies : first do no harm (to the commons) / Roger Brownsword -- When learning is continuous : bridging the research-therapy divide in the regulatory governance of artificial intelligence as medical devices / Calvin Ho -- The oversight of clinical innovation in a medical marketplace / Wendy Lipworth, Miriam Wiersma, Narcyz Ghinea, Tereza Hendly, Ian Kerridge, Tamra Lysaght, Megan Munsie, Chris Rudge, Cameron Stewart, and Catherine Waldby -- The challenge of 'evidence' : research and regulation of traditional and non-conventional medicines / Nayeli Urquiza Haas and Emilie Cloatre -- Experiences of ethics, governance, and scientific practice in neuroscience research / Martyn Pickersgil -- Humanitarian research : ethical considerations in conducting research during global health emergencies / Agomoni Ganguli-Mitra and Matthew Hunt -- Governance framework for advanced therapies in Argentina / Fabiana Arzuaga -- Human gene editing : traversing normative systems / Rosario IsasiRosario Isasi -- Modification and the future of health research regulation / Sarah Chan -- Human organs and animal bodies : regulating interspecies research / Amy Hinterberger and Sara Bea -- When is human? …”

Tabla de Contenidos: “…Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: What It's All About; Big data processing; The value of data; Historically for the few and not the many; Classic data processing systems; Limiting factors; A different approach; All roads lead to scale-out; Share nothing; Expect failure; Smart software, dumb hardware; Move processing, not data; Build applications, not infrastructure; Hadoop; Thanks Google; Thanks Doug; Thanks Yahoo; Parts of Hadoop; Common building blocks; HDFS; MapReduce; Better together…”

Tabla de Contenidos: “…1.2. e-Health, m-health, the Quantified Self and Big Data 1.3. Medical secrecy in the face of the computerization of healthcare data; 1.3.1. …”

Tabla de Contenidos: “…Complex similarity computationsRoute generation algorithms; Modeling across multiple domains; Summary; Chapter 4: Neo4j for High-Volume Applications; Graph processing; Big data and graphs; Processing with Hadoop or Neo4j; Managing transactions; Deadlock handling; Uniqueness of entities; Events for transactions; The graphalgo package; Introduction to Spring Data Neo4j; Summary; Chapter 5: Testing and Scaling Neo4j Applications; Testing Neo4j applications; Unit testing; Using the Java API; GraphUnit-based unit testing; Unit testing an embedded database; Unit testing a Neo4J server…”

Tabla de Contenidos: “…Machine generated contents note: Foreword Preface Acknowledgments Chapter 1 The Critical Role of Business Insight The Disruptive Nature of Data An Unconventional Look at Conventional "Wisdom" Innovating at the Speed of Data Weighing Risk and Bringing the Better Part of "Gut Instinct" Back into the Equation People, Process, Technology, and Culture Starting the Journey Notes Chapter 2 The Journey: Taking the First Steps toward Transforming Your Organization Different Approaches Juggling Multiple Challenges How to Deal with Challenges Effectively Executive Sponsorship: Critical to Success Understanding Current Capabilities Aligning Capabilities with Business Objectives Let's Start the Journey Taking the First Steps to Transforming Your Organization Note Chapter 3 Challenged Organizations: When Rugged Individualism and Department Silos Aren't Enough Getting Along One Day at a Time: Organizations at the Individual Level When "Have It Your Way" Isn't a Good Thing Superhighways and Dirt Roads Consolidated, but Not Cohesive: Organizations at the Departmental Level Subject Matter Experts and Gatekeepers Understanding the True Consequences of the Challenged Levels Business Transformation Strategy Objectives for Challenged Organizations Notes Chapter 4 Foundational Organizations: Making the Leap to an Enterprise-Wide Approach The Possibilities That Come with Patience Seeing the Value across the Enterprise How an Enterprise Level Organization Functions Big Data: The Big Opportunity for Enterprise Level Organizations Don't Let Up Continuous Improvement Required Business Transformation Strategy Objectives for Foundational Organizations Chapter 5 Progressive Organizations: Harnessing the Power of Information to Achieve Market Advantage and Expand Their Business Offerings Optimization: The Easiest Business Case of All Toward Innovation and Beyond Business Transformation Strategy Objectives for Progressive Organizations Chapter 6 Centers of Excellence: The Key to Accelerate Organizational Transformation The 10,000-Foot View of Information A Quick Look at the Key Responsibilities of a CoE CoEs and the Levels of Maturity How Should CoEs Be Organized? …”

Tabla de Contenidos: “…Front Cover; Wearable Sensors; Copyright Page; Contents; List of Contributors; Introduction; 1.1 Wearables: Fundamentals, Advancements, and a Roadmap for the Future; 1 World of Wearables (WOW); 1.1 The Role of Wearables; 1.2 Data-Information-Knowledge-Value Paradigm; 1.2.1 The Emerging Concept of Big Data; 1.2.2 Medical Loss Ratio and Wearables; 1.3 The Ecosystem Enabling Digital Life; 1.3.1 Smart Mobile Communication Devices; 1.3.2 Social Media Tools; 2 Attributes of Wearables; 2.1 Taxonomy for Wearables; 2.2 Advancements in Wearables…”

Tabla de Contenidos: “…Conclusion -- References -- Chapter 5 - Internal intelligence sources -- Information in This Chapter: -- Introduction -- Asset, vulnerability, and configuration management -- Configuration management -- Network logging -- The trouble with SIEMs -- The power of SIEMs -- Managed security service providers -- Access control -- Network monitoring -- Conclusion -- References -- Chapter 6 - External intelligence sources -- Information in This Chapter: -- Introduction -- Brand monitoring versus intelligence -- Asset, vulnerability, and configuration management -- Network logging -- IP addresses as pivot points -- Domain names as pivot points -- File hashes as pivot points -- Pivoting from MSSP alerts -- Network monitoring -- YARA -- Protecting against zero-day attacks -- Incident response and intelligence -- Collaborative research into threats -- Conclusion -- ReferenceS -- Chapter 7 - Fusing internal and external intelligence -- Information in This Chapter: -- Introduction -- Security awareness training -- Customer security awareness training -- OpenIOC, CyBOX, STIX, and TAXII -- OpenIOC -- CyBOX -- STIX and TAXII -- Threat intelligence management platforms -- TIMPs as a Rosetta Stone -- Big data security analytics -- Hadoop -- Conclusion -- Reference -- Chapter 8 - CERTs, ISACs, and intelligence-sharing communities -- Information in This Chapter: -- Introduction -- CERTs and CSIRTs -- CERT/Coordination Center -- US-CERT and country-level CSIRTs -- Company-level CSIRTs -- ISACs -- The ISACs -- Intelligence-sharing communities -- Conclusion -- References -- Chapter 9 - Advanced intelligence capabilities -- Information in This Chapter: -- Introduction -- Malware analysis -- Why it is a bad idea -- Setting up a malware lab -- Planning the network -- Virtual machines versus cloning -- Getting the malware to the lab -- Malware tools -- System tools -- Sandbox…”

Tabla de Contenidos: “…/ Gonzalo Menéndez Margolles -- Capítulo 15. La utilización de Big Data para la lucha contra el fraude fiscal y la protección de datos del contribuyente / Andrés garcía Martínez -- Capítulo 16. …”
Datos de acceso al recurso electrónico
Acceso a la plataforma Legalteca, busque el título una vez dentro.

Tabla de Contenidos: “…Araujo, Marco Antonio Silva,Valeria Arantes -- Resistance and moral development in community-based youth organizing / Ben Kirshner -- Moral education, big data and adaptive video games / John Tillson…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Contents -- Biography -- List of Contributors -- Chapter 1 Artificial Intelligence and Cybersecurity: Tale of Healthcare Applications -- 1.1 Introduction -- 1.2 A Brief History of AI -- 1.3 AI in Healthcare -- 1.4 Morality and Ethical Association of AI in Healthcare -- 1.5 Cybersecurity, AI, and Healthcare -- 1.6 Future of AI and Healthcare -- 1.7 Conclusion -- References -- Chapter 2 Data Analytics for Smart Cities: Challenges and Promises -- 2.1 Introduction -- 2.2 Role of Machine Learning in Smart Cities -- 2.3 Smart Cities Data Analytics Framework -- 2.3.1 Data Capturing -- 2.3.2 Data Analysis -- 2.3.2.1 Big Data Algorithms and Challenges -- 2.3.2.2 Machine Learning Process and Challenges -- 2.3.2.3 Deep Learning Process and Challenges -- 2.3.2.4 Learning Process and Emerging New Type of Data Problems -- 2.3.3 Decision‐Making Problems in Smart Cities -- 2.3.3.1 Traffic Decision‐Making System -- 2.3.3.2 Safe and Smart Environment -- 2.4 Conclusion -- References -- Chapter 3 Embodied AI‐Driven Operation of Smart Cities: A Concise Review -- 3.1 Introduction -- 3.2 Rise of the Embodied AI -- 3.3 Breakdown of Embodied AI -- 3.3.1 Language Grounding -- 3.3.2 Language Plus Vision -- 3.3.3 Embodied Visual Recognition -- 3.3.4 Embodied Question Answering -- 3.3.5 Interactive Question Answering -- 3.3.6 Multi‐agent Systems -- 3.4 Simulators -- 3.4.1 MINOS -- 3.4.2 Habitat -- 3.5 Future of Embodied AI -- 3.5.1 Higher Intelligence -- 3.5.2 Evolution -- 3.6 Conclusion -- References -- Chapter 4 Analysis of Different Regression Techniques for Battery Capacity Prediction -- 4.1 Introduction -- 4.2 Data Preparation -- 4.2.1 Dataset -- 4.2.2 Feature Extraction -- 4.2.3 Noise Addition -- 4.3 Experiment Design and Machine Learning Algorithms -- 4.4 Result and Analysis -- 4.5 Threats to Validity -- 4.6 Conclusions…”

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